How do you run analytics without moving our data?
dataface.ai reads your schema and a few example values per column, generates read-only SQL, and executes it on the source system itself. Your tables are never copied: no pipeline, no warehouse copy, no second place to secure.
Conventional analytics moves the data to the questions. dataface.ai moves the questions to the data.
What actually happens when someone asks a question
- Connect. A connector reads the schema: table names, column names, types, relationships, plus a handful of example values per column so the model knows what a status code or a date format looks like. Auto-discovery runs once and every connection is tested live before it is saved.
- Ask. The question and that catalog go to the model. The model returns SQL.
- Execute. That SQL runs on your database, on your infrastructure, under credentials you control.
- Insight. Only the result of that query comes back, through dataface.ai to the person who asked, formatted and charted. The tables it was computed from stay where they are.
What this removes
There is no ETL job to build, no warehouse to license, no nightly copy that drifts from the source, and no second copy of regulated data to secure, audit, and eventually delete. The governance surface stays where your DBAs already have it.
Why it matters commercially
The cost of an analytics stack is mostly the cost of the pipeline and the storage under it. Removing both is where the cost reduction on this page comes from, and it is why a new question does not need a new data engineering ticket.
Where this comes from
See it against your own data
A pilot is scoped to one governed use case and time-boxed to eight to twelve weeks, with success criteria agreed before it starts.
Get started freeRelated questions
- What is dataface.ai?dataface.ai is on-source conversational analytics. Ask a question in plain English, and it generates read-only SQL from your schema metadata and runs it on the systems you already own. No warehouse copy, no pipeline.
- What does the LLM actually see from our data?By default the model sees your catalog: table names, column names, types, and a few example values per column. It never receives your tables, and it only sees query results if a user turns on AI analysis.
- How do you stop an AI from writing to or damaging our database?Generated SQL is SELECT-only. INSERT, UPDATE, DELETE and DDL are blocked, queries are parameterized against injection, and every request passes both a workspace gate and a datasource gate.
- Can we run this entirely inside our own network?Yes. Enterprise agreements cover dedicated, VPC, and on-premise deployment, with edge execution on your own infrastructure at a reduced credit rate.